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• W96830120 abstract "In the Three Gorges of China, there are often landslide disasters, and the potential risk of landslides is tremendous. Thus, an efficient and accurate method of generating landslide susceptibility maps is very crucial to mitigate the loss of lives and properties caused by these landslides. This study presents a multidisciplinary approach to map landslide susceptibility on the Xietan Town of the Three Gorges, using slope units, intelligent models, geographic information system (GIS), and remote sensing data. Thirteen environmental factors, which have been extracted from 1:10,000-scale topographic maps, 1:50,000-scale geological maps, and HJ-1A satellite images with a spatial resolution of 30 m, were selected as predictor variables, including slope, aspect, curvature, slope unit altitude, engineering rock group, slope structure, distance from faults, land use, normalized difference vegetation index, reservoir water level, distance from drainage, catchment area, and catchment height. A two-class support vector machine (SVM) was trained and used to assess landslide susceptibility. Area under the curve was used to validate performance of the models. The results show that the two-class SVM outperforms the back propagation neural network in terms of both accuracy and generalization capacity, the area ratio being 0.8365 and approximately 90 % of landslides were classified as high and very high landslide-prone areas." @default.
• W96830120 created "2016-06-24" @default.
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• W96830120 date "2013-08-17" @default.
• W96830120 modified "2023-09-26" @default.
• W96830120 title "GIS-Based Landslide Susceptibility Mapping Using Remote Sensing Data and Machine Learning Methods" @default.
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• W96830120 doi "https://doi.org/10.1007/978-3-642-32618-9_23" @default.
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